For training, we use ProcTHOR to procedurally generate 10,080 houses. Each house has up to three rooms, entirely populated with Objaverse-LVIS assets except for structural components like doors and windows, which are inherited from ProcTHOR [1]}. We sample targets corresponding to LVIS categories for which a single instance is present in the scene, resulting in a total of 9,421 unique assets corresponding to 262 categories targeted during training. Training uses DD-PPO [2]} and is distributed across 28 GPUs on 7 AWS g4dn.12xlarge machines, with each
GPU hosting 360 houses and the subset of Objaverse-LVIS assets populating them. The training hyperparameters, identical to the ones in [1]}, and the 262 training target categories are listed in Table REF  and Table REF , respectively.
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